Software
This class will involve a number of computational homework assignments. These must be completed in quarto markdown files using the python language and be accompanied by a rendered pdf file. Teaching fluency in both R and python is an important goal of the program, and this course is devoted partially to building your python competency as it is a very standard language for machine learning applications.
Guide to Key Packages:
- sklearn is the most commonly used machine learning library in python
- statsmodels is a useful package for statistics in python
- quarto Document and website authoring app. Used for your homework and maybe project
- pytorch is a package for neural networks in python
- numpy Basic numerical python package
- pymc is a basic package for Bayesian stats in python
- marginaleffects is a very useful package for interpreting and communicating machine learning models in python
- matplotlib is a powerful plotting library in python
- seaborn is another powerful plotting library, though technically powered by
matplotlib. - islp Python package with the datasets of our textbook
- conda A good python package and environment manager
- mamba A fast drop in replacement for conda’s package installer
- pandas Standard python package for wrangling data
- polars Blazingly fast data wrangling library intended for larger datasets
- shiny Made initially for
Rbut it works with python now. Deploy ML apps - streamlit Package for making lightweight apps
- FastAPI Another lightweight production library